@article {10.3844/ajbbsp.2016.253.262,
article_type = {journal},
title = {3D-QSAR and SVM Prediction of BRAF-V600E and HIV Integrase Inhibitors: A Comparative Study and Characterization of Performance with a New Expected Prediction Performance Metric},
author = {Wesley, Leonard and Veerapaneni, Saihitha and Desai, Rachana and McGee, Francisco and Joglekar, Namrata and Rao, Sheela and Kamal, Zeeshan},
volume = {12},
number = {4},
year = {2016},
month = {Oct},
pages = {253-262},
doi = {10.3844/ajbbsp.2016.253.262},
url = {https://thescipub.com/abstract/ajbbsp.2016.253.262},
abstract = {The results of directly comparing the prediction accuracy of optimized 3D Quantitative Structure-Activity Relationship (3D-QSAR) models and linear Support Vector Machine (SVM) classifiers to identify small molecule inhibitors of the BRAF-V600E and HIV Integrase targets are reported. Performance comparisons were carried out using 303 compounds (68 active) against BRAF-V600E and 204 compounds (159 active) against HIV Integrase. A SVM prediction accuracy of 95% (BRAF-V600E) and 100% (HIV Integrase) and 3D-QSAR prediction accuracy of 76% (BRAFV600E) and 82% (HIV Integrase) was observed. To help explain the better performance of SVM in the comparison reported here and to help assess the degree to which a SVM or 3D-QSAR model is likely to perform best for other targetligands of interest a new EPP (Expected Predictive Performance) metric is introduced. How EPP can be used to help predict future performance of SVM and 3D-QSAR models by quantifying the degree of similarity between candidate compounds and training data is also demonstrated. Results show that the EPP metric is capable of predicting future prediction accuracy of SVM and 3D-QSAr models within 7% of actual performance.},
journal = {American Journal of Biochemistry and Biotechnology},
publisher = {Science Publications}
}